File size: 3,614 Bytes
38d5246 f51461a 38d5246 f51461a 38d5246 f51461a 38d5246 f51461a 38d5246 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 |
Dataset Metadata
dataset_info:
name: SC-NeRF
description: >
SC-NeRF is a dataset designed for 3D reconstruction using Neural Radiance Fields (NeRF) under a
stationary-camera setup. It targets high-throughput plant phenotyping in controlled indoor environments,
simplifying the traditional NeRF pipeline that requires a moving camera around static objects.
Instead, it uses a rotating object in front of a stationary camera, making it practical for automated
phenotyping systems. The dataset includes videos, extracted frames, COLMAP pose estimations, trained NeRF
models, and high-resolution point clouds for six agriculturally relevant objects.
version: 1.0
license: CC-BY-NC-4.0
authors:
- Kibon Ku
- Talukder Z. Jubery
- Elijah Rodriguez
- Aditya Balu
- Soumik Sarkar
- Adarsh Krishnamurthy
- Baskar Ganapathysubramanian
citation: >
@article{ku2025stationarynerf,
title = {NeRF-based Point Cloud Reconstruction using a Stationary Camera for Agricultural Applications},
author = {Kibon Ku, Talukder Z. Jubery, Elijah Rodriguez, Aditya Balu, Soumik Sarkar, Adarsh Krishnamurthy, Baskar Ganapathysubramanian},
year = {2025},
journal = {arXiv preprint arXiv:2503.21958}
}
intended_use:
- Stationary-camera-based 3D reconstruction
- High-throughput plant phenotyping
- AI-based point cloud generation
- Benchmarking indoor NeRF pipelines
- Hyperspectral and multimodal NeRF fusion
features:
- Videos (.MOV)
- Keyframes (JPG/PNG)
- COLMAP outputs (poses, sparse PCD)
- Trained NeRF models (nerfacto, Nerfstudio format)
- Final reconstructed 10M-point point clouds (.ply)
dataset_size:
raw:
- "6 video objects × 2 capture types (SC and GT) in .MOV format"
- "Keyframes extracted at 4–5 FPS per object"
pre:
- "COLMAP pose estimates and sparse point clouds for all objects"
train:
- "Nerfstudio-trained NeRF models with checkpoints"
pcd:
- "Final 10M-point point clouds for 6 objects (SC and GT), aligned and filtered"
dependencies:
- Python 3.8+
- Nerfstudio (https://docs.nerf.studio)
- COLMAP
- Open3D (for visualization and evaluation)
- CloudCompare or MeshLab (optional for inspection)
installation_instructions: |
Clone and set up the dataset locally:
```bash
git clone https://huggingface.co/datasets/BGLab/SC-NeRF
cd AgriPCD
```
download_instructions: |
1. Download the dataset files from the Hugging Face repository or provided links.
2. Unzip the folders:
```bash
unzip raw.zip
unzip pre.zip
unzip train.zip
unzip pcd.zip
```
training_instructions: |
Preprocess and train NeRF models using Nerfstudio:
```bash
ns-process-data --data ./pre/object_name
ns-train nerfacto --data ./pre/object_name
```
pointcloud_extraction: |
Export the high-resolution point cloud:
```bash
ns-export pointcloud --load-config ./train/object_name/config.yml
```
evaluation_instructions: |
Align reconstructed and ground truth point clouds using ICP and evaluate using precision/recall or other geometric metrics.
visualization_instructions: |
Visualize point clouds using Open3D:
```python
import open3d as o3d
pcd = o3d.io.read_point_cloud("pcd/apricot_sc.ply")
o3d.visualization.draw_geometries([pcd])
```
repository_links:
- https://huggingface.co/datasets/BGLab/SC-NeRF
- https://arxiv.org/abs/2503.21958
- https://docs.nerf.studio
|